MATLAB Code Implementation for Monte Carlo Simulation

Monte Carlo simulation is a mathematical computation method widely employed in research fields such as finance, economics, and management. This approach relies on random events and probability distributions to generate results through multiple simulation iterations. The simulation outcomes can be utilized for risk estimation, future trend forecasting, and decision-making processes. Although Monte Carlo simulations typically exhibit slower computational speeds compared to deterministic methods, they provide superior accuracy, making them particularly valuable for high-precision analysis and prediction scenarios. In MATLAB implementations, key functions include generating random numbers from specified probability distributions using functions like rand(), randn(), or random(). The core algorithm involves creating multiple scenarios through vectorized operations, often employing for-loops or parallel computing with parfor for large-scale simulations. Statistical analysis of output data can be performed using mean(), std(), and quantile() functions to derive meaningful insights from the simulated results.